Title: An AI-based clinical decision support system for management of dental treatment

Authors: Noha Algallai; Rami Muadab; Robert Flinton; Hind A. El-Hammali

Addresses: School of Health Professions, Rutgers University, 65 Bergen Street, Suite 120, Newark, NJ 07107, USA ' Department of Restorative Dentistry, Rutgers School of Dental Medicine, 110 Bergen St, Newark, NJ 07103, USA ' Department of Restorative Dentistry, Rutgers School of Dental Medicine, 110 Bergen St, Newark, NJ 07103, USA ' Department of Restorative Dentistry, Rutgers School of Dental Medicine, 110 Bergen St, Newark, NJ 07103, USA

Abstract: Dentists have diverse opinions about using AI-based clinical decision support system (CDSS) for abutment teeth selection used for attachment retained removable partial dentures (AR-RPD). Several studies have been done on factors affecting prognosis of abutment teeth. Dental decision support system (DDSS) helps in improving decision making process and provides substantial benefits in decreasing errors and assisting self-assurance in abutment selection. In this paper, we are proposing a DDSS based decision support system for managing dental issues. The system validation has been assessed by the knowledgeable prosthodontists. Cronbach's alpha and Pearson's correlation coefficient tests in SPSS were used to measure the system's reliability. Cronbach's alpha test displayed 0.918, which demonstrates internal consistency and agreement about the system's validity in creating the consistent and excellent quality management. Thus, the proposed DDSS will aid in determining the suitable abutment teeth used in AR-RPD to increase the patient's satisfaction.

Keywords: attachment retained removable partial dentures; AR-RPD; dental decision support system; DDSS; decision-making process.

DOI: 10.1504/IJAIH.2025.149257

International Journal of Artificial Intelligence in Healthcare, 2025 Vol.1 No.1, pp.75 - 91

Received: 17 Sep 2024
Accepted: 27 Jun 2025
Published online: 20 Oct 2025 *